class_docstr: Encode the categorical features to numerical features.
  
  # Arguments
      encoding: A list of strings, which has the same number of elements as
          the columns in the structured data. Each of the strings specifies
          the encoding method used for the corresponding column. Use 'int' for
          categorical columns and 'none' for numerical columns.
code: def __init__(self, encoding: List[str], **kwargs):
      super().__init__(**kwargs)
      self.encoding = encoding
      self.encoding_layers = []
      for encoding in self.encoding:
          if encoding == NONE:
              self.encoding_layers.append(None)
          elif encoding == INT:
              # Set a temporary vocabulary to prevent the error of no
              # vocabulary when calling the layer to build the model.  The
              # vocabulary would be reset by adapting the layer later.
              self.encoding_layers.append(layers.StringLookup())
          elif encoding == ONE_HOT:
              self.encoding_layers.append(None)
  
imports: from typing import List
  import tensorflow
  from tensorflow import keras
  from tensorflow import nest
  from tensorflow.keras import layers
  from tensorflow.keras.layers.experimental import preprocessing
  from autokeras.utils import data_utils
label_desc: Use image node_id 1 to create a new MultiCategoryEncoding object from inherited base classes: preprocessing with example: obj = MultiCategoryEncoding(encoding)